{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64b99e76-d14a-49fa-b067-c288627c31f3",
   "metadata": {},
   "source": [
    "This notebook is part of the **Practical Cheminformatics Tutorials** collection.  Additional notebooks can be found at [https://github.com/PatWalters/practical_cheminformatics_tutorials](https://github.com/PatWalters/practical_cheminformatics_tutorials)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e461d104-5e7d-4d7e-a559-79221caac19a",
   "metadata": {},
   "source": [
    "### Ten RDKit Tricks You Might Not Know \n",
    "<img src=\"https://raw.githubusercontent.com/PatWalters/practical_cheminformatics_tutorials/refs/heads/main/tricks_and_tips/robot_dog.png\" width=\"400\"/> \n",
    "<br/>\n",
    "A recent comment on LinkedIn asked about \"beyond the basics\" RDKit tutorials.  This isn't a proper tutorial, but does highlight a few intermediate to advanced tricks that some may find useful. Hopefully you'll find one or two things you haven't tried before.  The tips below range from one liners to slightly more complex examples.  However, most are less than a dozen lines of code. If you have additional RDKit tricks to share, please leave a comment.    \n",
    "\n",
    "[1. Make structures look better](#1.-Make-structures-look-better)   \n",
    "[2. Temporarily disable logging](#2.-Temporarily-disable-logging)     \n",
    "[3. Turn off highlighting after a substructure search](#3.-Turn-off-highlighting-after-a-substructure-search)      \n",
    "[4. Align a set of stuctures based on their Maxium Common Substructure (MCS)](#4.-Align-a-set-of-stuctures-based-on-their-Maxium-Common-Substructure)   \n",
    "[5. Get the Maximum Common Substructure (MCS) as SMILES](#5.-Get-the-Maximum-Common-Substructure-as-SMILES)   \n",
    "[6. Assign bond orders to a PDB Ligand](#6.-Assign-Bond-Orders-to-a-PDB-Ligand)   \n",
    "[7. Display Cahn-Ingold-Prelog stereochemistry](#7.-Display-Cahn-Ingold-Prelog-Stereochemistry)   \n",
    "[8. Get the largest fragment in a molecule](#8.-Get-the-largest-fragment-in-a-molecule)   \n",
    "[9. Create an R-Group table](#9.-Create-an-R-Group-Table)    \n",
    "[10. Show chemical structures and plots in a Pandas dataframe](#10.-Show-chemical-structures-and-plots-in-a-Pandas-dataframe)\n",
    "\n",
    "Note that each example it self contained, with the necessary imports in the first code cell. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b7f3e0-9d9f-4908-a1d0-a5f7a77deb38",
   "metadata": {},
   "source": [
    "#### Install the necessary Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "77da09a8-0bb7-4dab-9181-d0b3a7948694",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "import sys\n",
    "IN_COLAB = 'google.colab' in sys.modules\n",
    "if IN_COLAB:\n",
    "    !pip install rdkit useful_rdkit_utils pypdb seaborn tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9569b927-e01a-4baf-9dbd-dd794375c8c2",
   "metadata": {},
   "source": [
    "<a id='make_structures_look_better'></a>\n",
    "#### 1. Make structures look better\n",
    "The standard RDKit depiction code is pretty good, but sometimes the aesthetics leave a bit to be desired.  We can often dramatically improve the depiction quality with a couple lines of code.  There are two critical variables. \n",
    "- `IPythonConsole.ipython_useSVG` - tells the RDKit to draw structures and Scalable Vector Graphics (SVG) instead of images\n",
    "- `rdDepictor.SetPreferCoordGen` - tells the RDKit to use its alternate coordinate generator, which often produces better quality structures.\n",
    "  \n",
    "Let's begin with the default state,  which depicts structures as images using the default coordinate generator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "11135a05-1466-4245-a8a6-3bd8e799c11f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T11:11:51.397726Z",
     "start_time": "2025-06-02T11:11:51.389881Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x110fc14d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole, rdDepictor\n",
    "\n",
    "IPythonConsole.ipython_useSVG = False\n",
    "rdDepictor.SetPreferCoordGen(False)\n",
    "smiles = \"C=CC[N@@+]12CC[C@@]34c5ccccc5N5/C=C6/[C@H]7C[C@H]8[C@@]9(CC[N@@+]8(CC=C)C/C7=C/CO)c7ccccc7N(/C=C(/[C@@H](C[C@@H]31)/C(=C\\\\CO)C2)[C@H]54)[C@@H]69\"\n",
    "mol = Chem.MolFromSmiles(smiles)\n",
    "\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821106e8-761f-42e0-91e1-55e0956f3a2f",
   "metadata": {},
   "source": [
    "Adding these two lines makes the structure look better. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5c8616de-4325-4f3f-a61b-4293b47b7208",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T11:11:43.003482Z",
     "start_time": "2025-06-02T11:11:42.924135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x110fc14d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPythonConsole.ipython_useSVG = True\n",
    "rdDepictor.SetPreferCoordGen(True)\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69aa6315-f92f-41f8-b640-02397a031ab2",
   "metadata": {},
   "source": [
    "The [useful_rdkit_utils](https://useful-rdkit-utils.readthedocs.io/en/latest/jupyter.html) library has an easy to remember function that encapsulates these two lines.\n",
    "`uru.rd_make_structures_pretty()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9939d274-11e0-4c71-8847-4ece73ec0181",
   "metadata": {},
   "source": [
    "#### 2. Temporarily disable logging \n",
    "The logging feature in the RDKit can often be useful. However, there are occasions when it can be a bit to chatty and you just want it to shut the hell up.  This becomes particularly acute when using rdMolStandardize, which can be quite verbose.   Here's the default behavior of `rdMolStandardize`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76c16098-aa7e-4c40-93ba-52851e5ff342",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[08:26:21] Initializing MetalDisconnector\n",
      "[08:26:21] Running MetalDisconnector\n",
      "[08:26:21] Initializing Normalizer\n",
      "[08:26:21] Running Normalizer\n"
     ]
    }
   ],
   "source": [
    "from rdkit.Chem.MolStandardize import rdMolStandardize\n",
    "\n",
    "std_smiles = rdMolStandardize.StandardizeSmiles(\"c1cc(ccc1c2c(n(cn2)CC3CC3)c4ccnc(n4)N)F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7adf81-c66f-4c6d-a0ca-97c6287ee16a",
   "metadata": {},
   "source": [
    "This is not terrible, but if we're running through a large set of molecules, we'll end up with pages of messages.  A quick way to get rid of this is by using `rdBase.BlockLogs`.  Note the use of a [context manager](https://realpython.com/python-with-statement/) using the `with` statement. This only turns logging off within the indented block.  This way, you don't have to turn logging back on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5eb5189c-29e9-4787-947b-32bb40b67fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit.rdBase import BlockLogs\n",
    "with BlockLogs():\n",
    "    std_smiles = rdMolStandardize.StandardizeSmiles(\"c1cc(ccc1c2c(n(cn2)CC3CC3)c4ccnc(n4)N)F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e03e808-035e-43e5-907e-6d413940337c",
   "metadata": {},
   "source": [
    "#### 3. Turn off highlighting after a substructure search\n",
    "The `GetSubstructMatch` and `GetSubStructMatches` functions conveniently highlight the matching substructure when the a structure is drawn.  However, sometimes you want to be able to display the structure without the hightlight.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d9991c5a-617c-4a81-82b1-6ce7d5efe7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "Q 209.2 19.3, 209.5 18.7\n",
       "Q 209.9 18.1, 209.9 17.5\n",
       "Q 209.9 16.9, 209.5 16.6\n",
       "Q 209.2 16.3, 208.7 16.3\n",
       "Q 208.2 16.3, 207.9 16.5\n",
       "Q 207.5 16.7, 207.3 17.1\n",
       "L 206.6 16.8\n",
       "' fill='#0000FF'/>\n",
       "<path class='atom-21' d='M 213.6 7.5\n",
       "L 216.8 12.6\n",
       "Q 217.1 13.1, 217.6 14.0\n",
       "Q 218.1 14.9, 218.2 15.0\n",
       "L 218.2 7.5\n",
       "L 219.4 7.5\n",
       "L 219.4 17.1\n",
       "L 218.1 17.1\n",
       "L 214.7 11.6\n",
       "Q 214.3 10.9, 213.9 10.2\n",
       "Q 213.5 9.4, 213.4 9.2\n",
       "L 213.4 17.1\n",
       "L 212.1 17.1\n",
       "L 212.1 7.5\n",
       "L 213.6 7.5\n",
       "' fill='#0000FF'/>\n",
       "<path class='atom-22' d='M 137.9 46.2\n",
       "L 143.6 46.2\n",
       "L 143.6 47.3\n",
       "L 139.1 47.3\n",
       "L 139.1 50.3\n",
       "L 143.1 50.3\n",
       "L 143.1 51.4\n",
       "L 139.1 51.4\n",
       "L 139.1 55.9\n",
       "L 137.9 55.9\n",
       "L 137.9 46.2\n",
       "' fill='#33CCCC'/>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x33f2fc270>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles(\"c1cc(ccc1c2c(n(cn2)CC3CC3)c4ccnc(n4)N)F\")\n",
    "qmol = Chem.MolFromSmarts(\"c1ccccc1\")\n",
    "mol.GetSubstructMatch(qmol)\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "702c3c30-7acc-4435-9b15-90564b7fd9be",
   "metadata": {},
   "source": [
    "When the RDKit performs a substructure search, it sets an attribute call `__sssAtoms` to a list containing the atom numbers of the matching atoms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0a9a224e-898d-4802-8e20-8763be7f2ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol.__sssAtoms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753bbdff-8a2b-4678-8856-2e2ba34cccb3",
   "metadata": {},
   "source": [
    "To turn off the highlighting, we can simply set `__sssAtoms` to `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2c3dc46c-bc89-42cc-a036-b1bf0a84824b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "' fill='#33CCCC'/>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x33f2fc270>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol.__sssAtoms = None\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c048dc-a1b2-4651-9306-70761a549e0b",
   "metadata": {},
   "source": [
    "#### 4. Align a set of stuctures based on their Maxium Common Substructure\n",
    "When looking at a set of structures and trying to understand SAR, it's often useful to align the structures on a common core.  We can use GenerateDepictionMatchingTemplate to do this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4665752e-da56-413c-b263-fa2288dfff72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.rdFMCS import FindMCS\n",
    "from rdkit.Chem.Draw import MolsToGridImage\n",
    "from rdkit.Chem import AllChem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ea22edbc-c9ea-4548-aee7-05e97084fa32",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_list = ['CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cc3C)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cn3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(SC)nc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(C(=O)N)n3)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(Cl)n3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(OC)n3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccc3C)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccn3)n2)c(Cl)cc1C(=O)N']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b4324e7-ba0f-4129-8bdb-0152aff84d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the SMILES to RDKit molecules\n",
    "mol_list = [Chem.MolFromSmiles(x) for x in smiles_list]\n",
    "# define the parameters for the MCS calculation, if we're aligning it's import that the MCS only contains complete rings\n",
    "params = Chem.rdFMCS.MCSParameters()\n",
    "params.BondCompareParameters.CompleteRingsOnly=True\n",
    "params.AtomCompareParameters.CompleteRingsOnly=True\n",
    "# find the MCS\n",
    "mcs = FindMCS(mol_list,params)\n",
    "# get query molecule from the MCS, we will use this as a template for alignment\n",
    "qmol = mcs.queryMol\n",
    "# generate coordinates for the template\n",
    "AllChem.Compute2DCoords(qmol)\n",
    "# generate coordinates for the molecules using the template\n",
    "[AllChem.GenerateDepictionMatching2DStructure(m,qmol) for m in mol_list]\n",
    "# Draw the molecules, highlighting the MCS\n",
    "MolsToGridImage(mol_list,\n",
    "                highlightAtomLists=[m.GetSubstructMatch(mcs.queryMol) for m in mol_list],\n",
    "                molsPerRow=5,useSVG=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d7d452-8718-4b9d-96db-0a8e432f41af",
   "metadata": {},
   "source": [
    "#### 5. Get the Maximum Common Substructure as SMILES\n",
    "The function `rdFMCS.GetMCS` return the MCS as SMARTS.  Sometimes we'd like to compare the MCS for several groups of molecules (e.g clusters).  In these cases, it's handy to have the MCS as a SMILES.  The example below shows how to generate a SMILES representation of the MCS. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9c3c1612-5232-4401-80f4-459779db6149",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdFMCS\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f0cdc7-6035-4bf3-8801-7c895ec6caa0",
   "metadata": {},
   "source": [
    "We will begin with a list of SMILES and convert it to a list of RDKit molecules. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49dd44a7-b530-4e45-9f5e-df62e540fb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles_list = ['CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cc3C)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(OC)cn3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnc(SC)nc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(C(=O)N)n3)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(Cl)n3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cncc(OC)n3)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccc3C(=O)N)n2)ccn1',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccc3C)n2)c(Cl)cc1C(=O)N',\n",
    " 'CCOc1cc(Nc2nc(Nc3ccc4c(c3)CNC4=O)nc(Nc3cnccn3)n2)c(Cl)cc1C(=O)N']\n",
    "mol_list = [Chem.MolFromSmiles(x) for x in smiles_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32be3c5a-c4a8-47ee-99f6-dbf2623ac1b8",
   "metadata": {},
   "source": [
    "Now we can calculate the MCS and convert it to SMILES. \n",
    "- Set the parameters for the MCS calculation to only include complete rings. Otherwise, we will get aromaticity violations when we try to generate SMILES. \n",
    "- Calculate the MCS\n",
    "- Extract the query molecule from the MCS\n",
    "- Use the `ExtractSidechains` method to get the scaffold with labeled attachment points.\n",
    "- Convert the extracted molecule to SMILES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "aee10173-4896-4f69-9aaf-34f1d4776063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d923d719e9574b0985e35700e962c7fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[1*]Nc1nc(N[2*])nc(Nc2ccc3c(c2)CNC3=O)n1']\n"
     ]
    }
   ],
   "source": [
    "# set parameters for rdFMCS so that only complete rings are returned\n",
    "# this helps to avoid an MCS with stray aromatic atoms\n",
    "params = rdFMCS.MCSParameters()\n",
    "params.BondCompareParameters.CompleteRingsOnly=True\n",
    "params.AtomCompareParameters.CompleteRingsOnly=True\n",
    "# find the MCS\n",
    "mcs = FindMCS(mol_list, params)\n",
    "# get the MCS as a query molecule, this isn't what we want\n",
    "qmol = mcs.queryMol\n",
    "# match the MCS against the first molecule and delete parts that aren't\n",
    "# components of the MCS\n",
    "mcs_smi_list = []\n",
    "for mol in tqdm(mol_list):\n",
    "    mcs_mol = Chem.ReplaceSidechains(mol_list[0],qmol)\n",
    "    # get the SMILES for the MCS\n",
    "    mcs_smi_list.append(Chem.MolToSmiles(mcs_mol))\n",
    "print(list(set(mcs_smi_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e38f159-fbd8-4f6a-b422-4970ead6a5d6",
   "metadata": {},
   "source": [
    "#### 6. Assign Bond Orders to a PDB Ligand"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "599ef5ed-9dd0-4fd7-bde8-2444d1aedb79",
   "metadata": {},
   "source": [
    "I've written about this one [before](https://practicalcheminformatics.blogspot.com/2018/09/assigning-bond-orders-to-pdb-ligands.html), but it's worth revisiting.  In many cases, we want to read a PDB file and assign bond orders to a ligand structure.  If we know the SMILES for the ligand or if we know the Chemical Identifier (CID) from the PDB, we can use the function `AllChem.AssignBondOrdersFromTemplate` to assign the bond orders from a template molecule we created from the SMILES.  As an example, let's start with the PDB file below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b7dbce85-9cc4-41f5-b014-f5292a7a4154",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb_block = \"\"\"HETATM 2835  FA1 SB5 A 800       7.973  10.854  30.287  1.00 44.81           F\n",
    "HETATM 2836  CA1 SB5 A 800       6.115  12.249  30.931  1.00 49.28           C\n",
    "HETATM 2837  CA2 SB5 A 800       6.756  11.394  29.995  1.00 48.31           C\n",
    "HETATM 2838  CA3 SB5 A 800       6.139  11.100  28.755  1.00 49.84           C\n",
    "HETATM 2839  CA4 SB5 A 800       4.885  11.662  28.450  1.00 47.39           C\n",
    "HETATM 2840  CA5 SB5 A 800       4.211  12.533  29.376  1.00 49.11           C\n",
    "HETATM 2841  CA6 SB5 A 800       4.853  12.814  30.623  1.00 46.49           C\n",
    "HETATM 2842  CB1 SB5 A 800       1.349   8.914  30.280  1.00 53.54           C\n",
    "HETATM 2843  NB2 SB5 A 800       0.424   8.416  29.422  1.00 53.65           N\n",
    "HETATM 2844  CB3 SB5 A 800      -0.061   9.220  28.487  1.00 54.75           C\n",
    "HETATM 2845  NB4 SB5 A 800       0.276  10.494  28.302  1.00 55.77           N\n",
    "HETATM 2846  CB5 SB5 A 800       1.210  11.067  29.136  1.00 57.26           C\n",
    "HETATM 2847  CB6 SB5 A 800       1.797  10.262  30.198  1.00 52.51           C\n",
    "HETATM 2848  NB7 SB5 A 800      -0.952   8.708  27.683  1.00 52.79           N\n",
    "HETATM 2849  NC1 SB5 A 800       0.666  13.454  28.386  1.00 60.99           N\n",
    "HETATM 2850  CC2 SB5 A 800       1.578  12.466  28.945  1.00 60.39           C\n",
    "HETATM 2851  CC3 SB5 A 800       2.946  13.094  29.027  1.00 55.75           C\n",
    "HETATM 2852  NC4 SB5 A 800       2.872  14.416  28.563  1.00 57.42           N\n",
    "HETATM 2853  CC5 SB5 A 800       1.556  14.509  28.230  1.00 61.78           C\n",
    "HETATM 2854  CD1 SB5 A 800      -0.775  13.379  27.876  1.00 56.89           C\n",
    "HETATM 2855  CE1 SB5 A 800      -1.090  14.370  26.722  1.00 55.76           C\n",
    "HETATM 2856  CE2 SB5 A 800      -2.056  13.922  25.620  1.00 58.24           C\n",
    "HETATM 2857  CE3 SB5 A 800      -0.568  14.023  25.321  1.00 56.03           C\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc30fe88-b169-483a-81d9-9f444db65dd9",
   "metadata": {},
   "source": [
    "If we parse the PDB file and convert it to an RDKit molecule, we can see that all the bonds are set as single bonds.  This is the default when the RDKit parses a PDB file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7336ef40-5b2e-4dca-9535-d848f006349a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "import pypdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "52cc7892-de95-46a3-ad2d-1ca32a84777e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n"
      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x33ead50e0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read a molecule from a PDB block, if you you have pdb file use MolFromPDBFile\n",
    "mol = Chem.MolFromPDBBlock(pdb_block)\n",
    "mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9711431b-6d40-4e66-93bf-ba1cb33b59c8",
   "metadata": {},
   "source": [
    "To assign the bond orders, we'll use the following steaps. \n",
    "1. Get the CID for the ligand, this is the 4th token on each **HETATM** line.\n",
    "2. Use pypdb to get the SMILES corresponding to the CID\n",
    "3. Use `Chem.MolFromSmiles` to generate a template molecule from the SMILES\n",
    "4. Use `AllChem.AssignBondOrdersFromTemplate` to assign the bond orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bec26b0c-2955-4123-b76b-a80d2b930a24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:22] unrecognized bond type[08:26:23] WARNING: More than one matching pattern found - picking one\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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       "' fill='#0000FF'/>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x341bdd670>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the CID for the ligand, from the PDB file\n",
    "cid = \"SB5\"\n",
    "# pypdb has a function get the SMILES for the liagand from the CID\n",
    "tmplt_smiles = pypdb.describe_chemical(f\"{cid}\")['rcsb_chem_comp_descriptor']['smilesstereo']\n",
    "# convert tmplt_smiles to an RDKit molecule\n",
    "tmplt_mol = Chem.MolFromSmiles(tmplt_smiles)\n",
    "# use tmplt_mol to assign bond orders\n",
    "bo_mol = AllChem.AssignBondOrdersFromTemplate(tmplt_mol,mol)\n",
    "bo_mol"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b6b935-8475-45af-ac40-fb9f7372a7c3",
   "metadata": {},
   "source": [
    "<a tag='display-cip-sterechemistry'></a>\n",
    "#### 7. Display Cahn-Ingold-Prelog Stereochemistry\n",
    "The RDKit provides an easy, but not obvious, way of putting **R** and **S** labels on a structure. [This post](https://greglandrum.github.io/rdkit-blog/posts/2023-05-26-drawing-options-explained.html) from the RDKit Blog provides a great explanation of the many drawing options. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cd967d3b-16f2-41f1-8d2e-16e13934000d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\n"
      ],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x31b66cc80>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "IPythonConsole.drawOptions.addStereoAnnotation = True\n",
    "smi = \"O[C@@H]1CO[C@@H]2[C@@H](O)CO[C@H]12\"\n",
    "Chem.MolFromSmiles(smi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9aa5adf-b2c1-489d-9b92-c75ed7a2ab22",
   "metadata": {},
   "source": [
    "Of course, setting `addStereoAnnotation` to `False` turns the CIP labels off again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f0040189-fa64-4242-8af1-7a3418fe475d",
   "metadata": {},
   "outputs": [],
   "source": [
    "IPythonConsole.drawOptions.addStereoAnnotation = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39aa9e06-1e3d-459b-b3c9-c0d0ca1dd497",
   "metadata": {},
   "source": [
    "#### 8. Get the largest fragment in a molecule\n",
    "In many cases, we want to quickly remove counterions, waters of hydration, or other species that may be present in a molecule.  Note that the code below simply strips out all but the largest species.  If you want to neutralize charges, etc, try [rdMolStandardize](https://www.rdkit.org/docs/source/rdkit.Chem.MolStandardize.rdMolStandardize.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88bbd5e3-c48e-4964-b6fc-addb25bd65db",
   "metadata": {},
   "outputs": [
    {
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       "<path class=\"legend\" d=\"M 282.7 185.3 Q 282.7 184.0, 283.7 183.2 Q 284.8 182.5, 286.7 182.5 L 287.9 182.5 L 287.9 182.2 Q 287.9 181.2, 287.5 180.8 Q 287.1 180.3, 286.1 180.3 Q 285.4 180.3, 284.9 180.4 Q 284.4 180.6, 283.6 180.9 L 283.1 179.9 Q 284.6 179.1, 286.1 179.1 Q 287.8 179.1, 288.6 179.9 Q 289.4 180.6, 289.4 182.2 L 289.4 187.5 L 288.3 187.5 Q 288.3 187.4, 288.2 187.2 Q 288.2 186.9, 288.1 186.5 Q 286.9 187.7, 285.4 187.7 Q 284.2 187.7, 283.4 187.0 Q 282.7 186.4, 282.7 185.3 M 284.2 185.2 Q 284.2 185.9, 284.6 186.2 Q 285.0 186.5, 285.7 186.5 Q 286.3 186.5, 286.9 186.2 Q 287.5 185.9, 287.9 185.4 L 287.9 183.6 L 286.8 183.6 Q 285.5 183.6, 284.8 184.0 Q 284.2 184.4, 284.2 185.2 \" fill=\"#000000\"/>\n",
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       "<path class=\"legend\" d=\"M 313.8 179.3 L 314.0 180.4 Q 314.9 179.1, 316.3 179.1 Q 316.7 179.1, 317.3 179.3 L 317.1 180.6 Q 316.4 180.5, 316.0 180.5 Q 315.3 180.5, 314.9 180.8 Q 314.5 181.0, 314.1 181.6 L 314.1 187.5 L 312.6 187.5 L 312.6 179.3 L 313.8 179.3 \" fill=\"#000000\"/>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from operator import itemgetter\n",
    "\n",
    "smi = \"[Na+].[Na+].CN(Cc1cnc2nc(N)nc(N)c2n1)c3ccc(cc3)C(=O)N[C@@H](CCC(=O)[O-])C(=O)[O-]\"\n",
    "# convert the SMILES to an RDKit molecule\n",
    "mol = Chem.MolFromSmiles(smi)\n",
    "# return a list of connected fragments from the molecule\n",
    "frag_list = list(Chem.GetMolFrags(mol, asMols=True))\n",
    "# sort the list of fragements so that the largest is first, note the use of itemgetter to sort by the first list element \n",
    "biggest_frag_mol = sorted([(x.GetNumAtoms(), x) for x in frag_list],key=itemgetter(0),reverse=True)[0][1]\n",
    "Chem.Draw.MolsToGridImage([mol,biggest_frag_mol],legends=[\"before\",\"after\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8e0121-8d49-4c12-aedd-6abf3f66eac3",
   "metadata": {},
   "source": [
    "#### 9. Create an R-Group Table\n",
    "The RDKit makes it very easy to generate an R-Group table.  This example mirrors one in the documentation, but a feature as cool as this merits restating. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ade42113-8efc-4125-acfa-572a20cf00be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "import pandas as pd\n",
    "from rdkit.Chem import PandasTools\n",
    "from rdkit.Chem.rdRGroupDecomposition import RGroupDecompose"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded02b66-f534-4d61-bf83-f993fd770566",
   "metadata": {},
   "source": [
    "Create a Pandas dataframe from a csv block using StringIO. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "130da127-75d9-4cc8-a021-2a9dea352f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "buff = \"\"\"ligand_id,SMILES,dG_bind,dG_bind_err\n",
    "0,O=C(Nc1ccnc(NC(=O)C2CCC2)c1)c1c(Cl)cccc1Cl,-2.995,0.455\n",
    "1,O=C(Nc1ccnc(NC(=O)C2CCCC2)c1)c1ccccc1Cl,8.731,0.462\n",
    "2,O=C(Nc1ccnc(NC(=O)C2CCC2)c1)c1ccccc1Cl,3.316,0.448\n",
    "3,O=C(Nc1ccnc(NC(=O)C2CC2)c1)c1c(Cl)cc(Cl)cc1Cl,-0.07,0.462\n",
    "4,O=C(Nc1ccnc(NC(=O)C2CC2)c1)c1cc(Cl)ccc1Cl,3.431,0.449\n",
    "5,O=C(Nc1ccnc(NC(=O)C2CC2)c1)c1c(F)cccc1Cl,-5.422,0.442\n",
    "6,Nc1cccc(Cl)c1C(=O)Nc1ccnc(NC(=O)C2CC2)c1,7.269,0.444\"\"\"\n",
    "df = pd.read_csv(StringIO(buff))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d366b6-cf49-4c2a-b265-74366baeca57",
   "metadata": {},
   "source": [
    "Create a list of molecules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8191fecc-4362-4b01-8edb-c54a070baaea",
   "metadata": {},
   "outputs": [],
   "source": [
    "mol_list = [Chem.MolFromSmiles(x) for x in df.SMILES]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb4cc2d-67c4-45b9-bd68-08096a7b2ffa",
   "metadata": {},
   "source": [
    "Create a scaffold molecule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b58d5c9-dc50-4ff5-b1d5-05b73ca148d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaffold = Chem.MolFromSmiles(\"O=CNc1ccnc(NC=O)c1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4188e92d-76cc-4d92-b365-8a40d6f2fb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "<rdkit.Chem.rdchem.Mol at 0x31b220dd0>"
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     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaffold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2358da6a-1dc0-4328-9619-aff810661a26",
   "metadata": {},
   "source": [
    "Perform the R-Group decompositiion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "32a55be8-ce01-4c68-a718-6940422571eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "rg,_ = RGroupDecompose([scaffold],mol_list,asSmiles=False,asRows=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e653712d-88f7-4a3d-b7d8-0cec51d0debc",
   "metadata": {},
   "source": [
    "Put the molecules, R-Groups, and the core into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "53893b8b-408d-4559-afbf-caacfe603865",
   "metadata": {},
   "outputs": [
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       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "      <td style=\"text-align: center;\"><div style=\"width: 200px; height: 200px\" data-content=\"rdkit/molecule\"><img src=\"\" alt=\"Mol\"/></div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Mol  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x33425b680>   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x33425b300>   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x33ead7a00>   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x33ead7bc0>   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x33ead7760>   \n",
       "5  <rdkit.Chem.rdchem.Mol object at 0x33ead6a40>   \n",
       "6  <rdkit.Chem.rdchem.Mol object at 0x33ead7680>   \n",
       "\n",
       "                                            Core  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x33425b450>   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x33ead5e70>   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x33ead7530>   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x33ead75a0>   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x33ead7370>   \n",
       "5  <rdkit.Chem.rdchem.Mol object at 0x33ead7060>   \n",
       "6  <rdkit.Chem.rdchem.Mol object at 0x33ead71b0>   \n",
       "\n",
       "                                              R1  \\\n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x33ead7300>   \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x33ead74c0>   \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x33ead5e00>   \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x33ead4820>   \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x33ead6c00>   \n",
       "5  <rdkit.Chem.rdchem.Mol object at 0x33ead6c70>   \n",
       "6  <rdkit.Chem.rdchem.Mol object at 0x33ead5a80>   \n",
       "\n",
       "                                              R2  \n",
       "0  <rdkit.Chem.rdchem.Mol object at 0x33ead6ce0>  \n",
       "1  <rdkit.Chem.rdchem.Mol object at 0x33ead6d50>  \n",
       "2  <rdkit.Chem.rdchem.Mol object at 0x33ead6dc0>  \n",
       "3  <rdkit.Chem.rdchem.Mol object at 0x33ead6f80>  \n",
       "4  <rdkit.Chem.rdchem.Mol object at 0x33ead6650>  \n",
       "5  <rdkit.Chem.rdchem.Mol object at 0x33ead6ea0>  \n",
       "6  <rdkit.Chem.rdchem.Mol object at 0x33ead6960>  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg_df = PandasTools.RGroupDecompositionToFrame(rg,mol_list,include_core=True)\n",
    "rg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91058c8c-b6fc-4041-afed-c34ff48d3643",
   "metadata": {},
   "source": [
    "#### 10. Show chemical structures and plots in a Pandas dataframe\n",
    "In many cases, I want to look at chemical structures together with an image.  For instance, I might want to look at structures, IC50 data, and a dose response curve.  In the example below we'll look at how to add a chemical structutre image and plot to each row in a dataframe.  In this case, the images will be boxplots depicting the similarity of each molecule to all the molecules in the dataset. This tip takes advantage of two functions in the [useful_rdkit_utils]() library.\n",
    "- `uru.mol_to_base64_image` - converts an RDKit molecule into a base64 encoded image.\n",
    "- `uru.boxplot_to_base64_image` - takes a distribution as a list or numpy array and return a base64 image with a boxplot.  It should be easy to modify this function to generate other types of plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ec0de5a7-6ddb-4444-9443-28f2d9af88cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import useful_rdkit_utils as uru\n",
    "from rdkit import Chem\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem import rdFingerprintGenerator\n",
    "from tqdm.auto import tqdm\n",
    "from IPython.display import HTML\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43efb6a0-2b6c-44a3-9fb9-49f61c79bc19",
   "metadata": {},
   "source": [
    "To begin, we'll grab some data.  In this case, a SMILES file containing the structures of some marketed drugs.  For simplicity, we'll only look at 100 compounds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a56f81e9-9aa6-4b79-b3fd-c670d33e3aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_df = pd.read_csv(\"https://raw.githubusercontent.com/PatWalters/datafiles/refs/heads/main/chembl_drugs.smi\",\n",
    "                sep=\" \",names=[\"SMILES\",\"Name\"])\n",
    "sim_df = sim_df.head(100).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23124198-409c-471d-955e-4939a54b2c73",
   "metadata": {},
   "source": [
    "We are goign to calculate the Tanimoto similarity of every molecule to each of the molecules in the dataframe.  To do this, we need to instantiate a `rdFingerprintGenerator.MorganGenerator` object, which will calculate Morgan finerprints. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e2d4e898-a46d-4b40-82a2-1a057d9ff205",
   "metadata": {},
   "outputs": [],
   "source": [
    "morgan_generator = rdFingerprintGenerator.GetMorganGenerator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "648d6e23-c6ec-4395-b4ea-abf7ed2fc17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf3fdea2c9df44c1b027b0db603e0db5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize the list of images\n",
    "img_list = []\n",
    "# create a list of molecules, the method uru.get_largest_fragment is the same as #8 above\n",
    "mol_list = [uru.get_largest_fragment(Chem.MolFromSmiles(x)) for x in sim_df.SMILES]\n",
    "# create list of fingerprints\n",
    "fp_list = list(map(morgan_generator.GetFingerprint,mol_list))\n",
    "# iterate over molecules and fingerprints and generate images for the molecule, and boxplot of the similarity distribution \n",
    "# for the molecule\n",
    "for mol,fp in tqdm(zip(mol_list,fp_list),total=len(fp_list)):\n",
    "    # create a list of similarities for this molecule vs all the rest\n",
    "    sim_list = DataStructs.BulkTanimotoSimilarity(fp, fp_list)\n",
    "    # create an image of the molecule\n",
    "    mol_image = uru.mol_to_base64_image(mol)\n",
    "    # create a boxplot of the distribution in sim_list\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n",
    "        boxplot_image = uru.boxplot_base64_image(sim_list,(0,1.1))    \n",
    "    # add the images to img_lsit\n",
    "    img_list.append([mol_image,boxplot_image])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6de992-9864-4598-9548-9633fd46b03a",
   "metadata": {},
   "source": [
    "Add the images to the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cd0a1797-4349-4336-b310-2eae9f4449ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_df[['mol_image','boxplot_image']] = img_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3a6a37-d9b8-4ca5-b469-2ac6ea47c051",
   "metadata": {},
   "source": [
    "Display the dataframe with the images. I didn't fee the SMILES were helpful for this visualization, so I dropped that column from the dataframe when displaying it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a7eb57d1-bf66-4388-be1c-cb75904a6a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>mol_image</th>\n",
       "      <th>boxplot_image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL700</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL1200982</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL1200728</td>\n",
       "      <td><img src='data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAASwAAACWCAIAAADrOSKFAAAABmJLR0QA/wD/AP+gvaeTAAAP0ElEQVR4nO3de1BU5/0G8IflpqAQ8VaUNCrx2jCDWrBNAwrqGC2ZMelgpWXbiXTAGIWYGDG26a6XAkabbEGbgEbitRkUY1CRjJd2vJdoHRW8hWpaTbxFi4sYVljO7489cX9EZC8s+55zeD7DH+9w3vecL+Djub/rI0kSiEgcnegCiDo7hpBIMIaQSDCGkEgwhpBIMIaQSDA/0QWQAwUFuH9fbsfF4dlnHfSvrUVhodyeMAGjR9sXnT6Nbdvktl6PyEjHW5ckLFokt6OjMXWq03WT8yRStt69JUD+Cg+Xamsd9P/3v+398/NbLFq/3r6oosKprVut9iG/+Y2bPwK1jYejanLtmn2/RJrBEKpMfj7+9S/RRZBHMYQqY7UiIwNWq+g6yHMYQtUYOVJuHD+ODz4QWgp5FEOoGvHxmDJFbi9ciK+/FloNeQ5DqCZ5efD1BQCzGW+8Iboa8hCGUE2iopCWJrc//hjl5UKrIQ9hCFUmNxc9e8rtrCw0NAithjyBIVSZsDC8/bbcrqlBbq7QasgT+Nia+syahaIinD0LAMuWISUFw4a5vJL//Q/XrzvuxnkXvIAhVB9/f7z/PsaNgyTBYkFGBv7xD/j4uLaSlJSOKY5cx8NRVYqPt6fowAFs3Ci0Gmof7gnVasUK7NqFu3cBYN48JCWhRw8XhgcHw8+5P75tE9RxuCdUq/Bw+8PcN2/CaHRteGkpamsdf9254+m66REMoYrNnm1/lu2vf8WZM0KrIXcxhCrm64vCQuh0ANDUhLlzRRdEbmEI1S0mBi+/LLf37cPOnUKrIbcwhKr3zjvo1Utuz5uHpiah1ZDrGELVCwtDTo7cvnABH34otBpyHUOoBWlp+MlP5HZ+vtBSyHUMoRbodCgqku/78ZFu1WEINSIqCq+8IroIcgtDqB1Ll6JfP9FFkOsYQu0ICcGyZaKLINcxhJqSmorERNFFkIv4ALfSrVkDiwUABg92qv9HH+HYMbn98KE2m7FjsXWr3I6OdmptOp19yFNPOTWEXOUj8bVNIqF4OKomO3ciIwM7drSyqKkJGRl49VWv10TtxhCqyfHjKCrCiROtLLJaUVTEx2VUiSEkEowhJBKMISQSjCFUH1cnViOFYwg1wnaniflUI4aQSDCGkEgwhpBIMIZQTXjip0kMoUYwn+rFEBIJxhASCcYQEgnGEKoJT/w0iSHUCOZTvRhCIsEYQiLBGEIiwRhCNeGJnyYxhBrBfKoXQ0gkGENIJBhDSCQYp8FXk9DQz+PiToaGxgAjH1nYFBe3NiDAD5ghoDJqB+4J1aS29tODBzPM5vJHF0lSw8GDGf/8Z5b3q6J2YgiJBGMIiQRjCIkEYwjVxPY5dj68Ja8tDKFGMJ/qxRASCcYQEgnGEBIJxhCqCU/8NIkh1AjmU70YQiLBGEIiwRhCIsEYQjXhiZ8mMYQawXyqF0NIJBhDSCQYQ0gkGEOoJjzx0ySGUCOYT/ViCNXq2LFjgwcPHjRo0Pz5820JJJViCNXHYrEsWLBg3LhxNTU1ly9fXr58eXx8/KlTp0TXRW7ivKPqs2LFirq6Oh8fn4SEhIiIiD179hw6dCghIQFAc3Oz6OrIZdwTqkZlZWVJSQmAurq6mJiYw4cP79+/f/369TU1NQaDISAgAEB9fb3RaLRYLKKLJVdIpHhXr17V6/W2iy79+/dft25dc3Pz9/pcvHgxOTnZ9jcdPHhwSUmJkFLJDQyhot2/fz8vL69bt24Aunbtmp2dbTab2+i/d+/eZ555xhbF8ePHnzlzxmulktsYQuUqKysbMGCALVFJSUmXL192ZlRjY2NhYWGvXr0A+Pn5paen37p1q4MrpXZhCJXoxIkTcXFxtviNGjXqwIEDrq7h9u3bmZmZvr6+AMLCwkwmU1NTU0eUSu3HECrLrVu3HoanZ8+e7QzP2bNnJ02aZAvz8OHDKyoqPFgqeQpDqBQPHjwwmUwhISEA/P39MzMza2trPbLmsrKyQYMGPTysvXTpkkdWS57CECpCWVlZZGSkLScTJkyorq727PotFovJZOrevTuAgICAzMzMti/wkDcxhIKdO3du8uTJtvgNHTp0165dHbetr776Kj09XafTAejXr19hYaHVau24zZGTGEJhvvlGMhqP+/n52U7/Vq5c2djY6IXtHjlyJCYmxhb7qVNfr6z0wjapLQyhAI2NUmGh1Lu31KWLNGBAQnp6+s2bN71ZQHNzc0lJyZNPPhkbe93HR9LrpWvXvLl9aoEh9LaKCmn4cAmQAGniRKm6ukFUJWbzvexsKTBQAqTu3aVly6QGYbV0agyh91y8KCUny/F7+mlJIQ+W1dTYq4qMVEpVnQpD6A11dZLBIO9zgoMlg0Fx+5x9+6SoKDmKiYnS6dOiC+pMGMKOZbVK69ZJfftKgKTTKfrs6+GZKiD5+Unp6ZJ3T1Q7L4awAx07Jo0ZI+9exoyRjh4VXZAT7tyRMjMlPz8JkHr0kEwmySuXbDs1H0nozAiHD+PVV+V2bi6+u2HmwNixuHsXAOLiUFDg/tbffBN//7vcHjIEmzbB4RQtkyfj1i0AmDgRubmP7Xb1KhYuxMaNkCREROBPf4Je73jlynH+PF5/Hbt3A8CwYXj33bb+NEeOwGCQ2z4+WL0aTz3lYP1r1+Jvf5PbO3ciMNC+KC0NX34JAEOG4P33nap20yasXSu3161DRIRToxRE7P8B5eXyjgKQNm50dlSPHvKQKVPatfUXX7RvHZA++sjxkH795M7TprXeob5eMhikrl0lQAoKkrKzpbq6dhUpUFmZFBkp/7xJSVJNTevdtm9v8WucPNnxmhcutPevr2+x6OGl41GjnK1zyRL72s6fd3aUcvDNers33sA337g/XJKwZQtGjMCiRWhoQHIyzp5FXh66dfNcid71wgs4dw4mE0JCsHMnhg9HVhbMZgejdu/GJ594pT6tYAjtbt/GW2+5OfbECcTFYdo0/Oc/GD0aBw6gpMTxUZny+fsjKwvnzyM9HVYr8vMxbBiKitD2XDZZWair81aJ6scQAoDuu1/Dhx/i0CHXxl67howMxMbi8GGEh6OwEJWVeO45j9co0sOf62c/a/HzPs6VK/azRHKIIQSAqCjYJoWQJMycicZGZwcWF2PIEBQVISAAb72FixeRnm6PtMaMHo2DB7F+Pfr1k/f8v/0tmppa9Jk6VW7k5+PkSe/XqEoa/ffiIl9f5OTI7epqvPuuswN/+EPcu4ekJFRXIydHxad/TvLxgV6PL76AwYDAQDx4AL+Wk2bOn4++fQHAakVGBqxWIWWqDEMoe+EFjB0rtxcvlq+SOzR+PE6exI4d+O6l2U4hKAhGI86exYoV31/UrRv+8Ae5/fnnKCrycmmqxBDamUzw9QWA+/fxyivOjoqO7riKFG3gQPTv38r3Z85EVJTcXrAAX3/tzaJUiSG0i47G734ntysqsG2b0GpUy88PJpPcNpvx5ptCq1EDhrCFnBz07Cm3Z8+Wn8shVyUm4sUX5fbmzSgvF1qN4inosyj27XN8I9im42Z5DwuDwYDMTAC4dg1GI957r6O2pW1//jMqKvDttwCQlYXERHTp4toaamuxZYtTPaurXS5PURQUwuJiFBeLLgKYNQvFxfLl9YIC6PUYNUp0TSo0cCCys2E0AkBNDXJzsWiRa2u4dAnTpnVAZcrDw9Hv8/VFYaF8r4/X2dtjwQIMHSq38/Jw7pzQahRMQXvC+HhnL/Rv3owHDzqwkpgYzJiBNWsA4PhxrF6NmTM7cHNaFRiIv/wFzz8PAA8eYM4c7N3rwvDQUHw3C7kDX3yBCxfcqVAhFBTC9HT8+tdO9fz0044NIYBly7B9u/w89+9/j+Rk+wUbct6kSfjFL1BaCgD79mHbNrz0krNjIyOxY4dTPZcuxdtvu1mhEvBwtHVhYfZnaO7cwZIlQqtRM5PJ/iDRvHloaBBajSIxhI+Vloaf/lRur1qFqiqh1ahWRAT++Ee5ffkyrza3giF8LJ0Oq1bJz9A0NWH+fNEFqdZrr+FHP5LbOTm4fl1oNcrDELZl5EjMmiW3d+/Gnj1Cq1Etf3988IE8u8e9e/J9C3qIIXRgyRL84AdyOzvbwcus9DjPPWe/6rZmDY/tW2AIHQgNxfLlcvvkSR5KuW/FCjzxBABYrc5e9uwkFHSLwrOuXkVlJa5cAYBBgzBuHLp3d3NVqakoLsb+/R6srjPq2xdLlmDOHAAQMsXf6dP48kvcvIk+fTBmjPzeoxJoMITXr2POHGzf3uKl79BQGI3IynJz3sGCAkRHu/DGPbVq1ixs2IDKSm9vd8sWLF7c4hjY1xe//CUKChAW5u1iHqW1w9FLlxAbi61bAWDKFLz2GvR69O2Lu3cxdy7eecfN1Y4YgblzPVhmJ2W74Ozl6T+MRkybhqoq9OmDqVORmorhw2G1YvNmJCaivt6rxbRKayHMzMSVK4iMRFUVdu3Ce+9h/XpUVcmv3i5ahJs33VyzwYABAzxXaGf14x/bX9r0gmPHsHgxAGRn48oVfPIJNmxAVZX8+v+pU1i50nvFPI7WQjhjBuLisG2b/dFhAL16yfvAb7/FZ5+5ueagoFZmcyA35Oaid28vbSs0FCkp0OuRl4eAAPmbOh2MRvlfiO2gSSzB0+AD9ncUdDpnT9iam+Uzex8fZ49tzGaEhgKA0cjZ+DyssRH37sntkBD58Ya21dfbn/594okWf/c7d+STeX9/9OjhVAH379sL6NnTqQIAzJiB4mKEhqK21qn+HUf8hRknf2X/nxsnFQEB0OnQ3OzO5qhtzqfloeBgBAe3vsiNKyVBQQgKcnmU21fLPU5rh6OPc+GCfJ/96adFl0LKcPEiAEVMk9dZQrhhAwAEBmLiRNGlkAJcvy5/IJftdUexOkUI//tfrF4NAC+/zNcCCQByc2GxoEsX+yfzCaT9EDY2Yvp0mM0ID8fSpaKrIQUoL5c/1tJgaH3qVC/TeAgbG/GrX+HoUXTrhtJS7gYJR48iJQWShJ//XClzomo5hBYLUlKwdSu6dkVZmf0NXeq0Dh3C88/DbMazz+Ljj5VyqVyzIbxxAwkJKC1FcDBKS5GQILogEm3jRkycCLMZiYn47DMFfXqPNkN4/DhiY3H0KAYOxOHDbX3eOnUGTU1YsAB6PRoaMHs2KioUlEAo4Wa9xxUVITMTFgvi47FlC/r0EV0QCXXjBlJTsXcvAgOxahXS0kQX9Ajxj615kMWC6dOxfTsABAVh+vTvf3oegPBwTq/Qiezdi5QUeerKl15CbGwrfZKTBd+y11QIb9ywT0XxOCNGqP6jC8h5qanYtMlBn/JywScsmjocDQlBYaGDPq4+5UiqlpSEiAgHfYQ/uaapPSGRGmnz6iiRijCERIIxhESCMYREgjGERIIxhESC/R9KCWlLYSD0IQAAAIN6VFh0cmRraXRQS0wgcmRraXQgMjAyNC4wMy4zAAB4nHu/b+09BiAQAGJGBghgAWJmIG5gZGfIALGZGNkYNEAMFqgAIxOcwc3AqMHEyKTAyKzgBNIsboVkEgPL9e2BQIEGexBnwqGvG4FsZRD72/RY+2B1hv0gdsqZ2P3RG//vA7HFAHi2FXsXG1JcAAAAvHpUWHRNT0wgcmRraXQgMjAyNC4wMy4zAAB4nH2Q0QrDIAxF3/2K+wMdmVqpj1XLGKMWtm7/sPf9P0taXFtaFhVurichqiBxT7f3B7/QSSmA/mzvPV6GiFQPEQjd5ZoRxzYUJw7PPD5gYbiC15Zsx6EvzhkZdOJmDXEigmN2JlE4jYiK9vc70EwNG1c7drmiJnKHoGWwEtLZmbTe20Oyy2kz8/yKMOS0vELLWYYVwywjSWrX7dbFkpfPY62+hodNfNXF3SoAAABXelRYdFNNSUxFUyByZGtpdCAyMDI0LjAzLjMAAHicFcgxCgAgDATBr1gqnOECMShiZZ8X+Xi12C0m1s5RIp1MIdlfqlBMgm/SvTkqpZGOWT+4fbAxDOVcskANNNc0KpIAAAAASUVORK5CYII='/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL590</td>\n",
       "      <td><img src='data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAAASwAAACWCAIAAADrOSKFAAAABmJLR0QA/wD/AP+gvaeTAAAQMUlEQVR4nO3de0xUZ/4G8GeQuxdAReVuvaBoRSt4pd4oTS1arV3RpOlsm7Udt5ss26ZpSIyVbPQPdjXNNNvbaNeUpiZb3HYNrW1TVGJsFSpEbREE7QKjgIKg/TlTYAaG3x+vPTsdhxF15ryHmefzB5E5M2e+4DznfM/7Hs7RDQwMgIjkCZJdAFGgYwiJJGMIiSRjCIkkYwiJJGMIiSQLll0A3aGlBeXlaGlBSAimTEF2NsaMkV0T+RBDqCU9PfjLX/DPf6K/H0FBcDgAICoKO3fiz3+WXRz5CttRzRgYwKZN2LsXv/sdamrQ3w+7HWVlSElBfj727JFdH/mKjmfMaMWnn2LjRmzahH/9Czrd/x6/eRPz5qGtDY2NiI+XVx/5CveEmrF/PwD89a+/SSCA6Gi8/jpsNhw4IKUu8jWGUDMqKzFpEmbOdLMoO/v2E8gfMYTaYLOhsxOJie6XJiUBQFubmhWRahhCbRBH5i6NqCIoCAD6+9Wrh1TEEGpDWBhGj0Z7u/ul164BwIQJalZEqmEINeORR2A2u+85T50CgPnzVa6I1MEQasbmzRgYwNtvuz7ucOAf/4BOh82bZZRFPscQasYf/oBZs/D3v+O9926fKwPAYsFLL6GiAi++iLQ0qfWRr3CyXkuamvDUU6ipQVwc5s1DdzeqqmCxYPNmfPghwsNl10c+wRBqTF8fSkpQVobmZoSFYdo05OVh+XLZZZEPMYQa09EBsxmxsUhOBoDz59HTg9mzuRv0Yzwm1JiDB5GZib/97fa3zzyDzEyYzVJrIt9iCIkkYwiJJGMIiSRjCDXG80mk5I8YQm1jJgMAQ0gkGUNIJBlDqDHsPwMPQ0gkGUNIJBkv/qstP4eFhaSl2UaPjgYA/F9SUnBwcHBQUKjkusiHuCfUlo96ekbW1e2wWMS3GWbzyLo6M0+y92sMIZFkDCGRZAwhkWQMIZFkDKGmiese6Dh379cYQm1h6gIQQ0gkGUNIJBnPmNEim822e/fuUaNGyS7EvfPnz1dWVk6bNm05r8XoDQyhFn3wwQd9fX0AQkJCZNfyG3a7fffu3du3bxfHrs8999w777wzZswY2XUNcwOkGWfOnEn79Vr3oaGhQUFBAHQ63fPPP9/Z2Sm7uoHS0tLp06eL8sLCwsQ/4uPji4uLHQ6H7OqGMYZQE65fv56fnz9ixAgAY8eONRqNNpvt+vXrBQUFoaGhAGJiYoqKinp7e6WUd+HChdzcXJG61NTUQ4cODQwMnDp1KisrSzyYkZHx7bffSqnNDzCEktlsNqPRGBUVBSAkJMRgMHR0dDg/wTkAM2bMOHz4sJrldXV1edgQOByOkpKS5ORkscfOy8trbm5Wszz/wBDKVFZWNnv2bBGwnJycmpoaD89UOtW1a9deunTJ17XZ7XaTyRQbGwsgKChIr9dfu3ZNLHI4HM79p9VqLSwsDA8PBxAZGVlYWNjd3e3r8vwJQyhHfX39mjVrlAbv888/v+tLxD5TjIKEhITk5+f//PPPPirv6NGjc+bMEeWtWrXq3LlzyqLq6uply5bt27fP5SVms1mv14uXJCUlFRcX+6g2/8MQqs25wYuOji4qKurp6Rn6y1tbWw0Ggzh6jIuLM5lMfX19Xizv4sWLeXl5IkvTpk0rKSlRFrW1tW3ZskUMF82ZM8ftYMyxY8fS09PdppcGwxCqp7+/v7i4eMKECXc2ePeqqqrq0UcfVQZFTpw48eDlWSyWwsJCMew5cuRI567S5cA1Pz//5s2bg63Hiz9mgGAIVeKyizh79uwDrlAMiqSkpCiDIk1NTfe3KhGbSZMmiVXp9fq2tjZlqfPhaE5Ozvnz54eyTs8jOuSMIfS5S5cuKQ2e1w+WxKBIRESEMijyyy+/3NMaKisrFy9eLMpbuHDhyZMnlUV1dXUPODArd2h3uGAIfUg0eGLY0KXB867Lly/r9XrxtxeJiYlDnD13flVCQoLzq7q6uvLz84ODg72yH3PZl9bW1t73qvwSQ+gTDofDucHLy8szm82+ftPy8vK5c+eKz/rKlSs9dLxWq7WoqEicmxoZGVlQUHDr1i2xyHlmIjg42GAwtLe3P3htag7tDjsMofdVVlYuWbJEhGHBggXODZ6v3XVQxPlIUkw5NjY2KkuPHDmizExkZ2f/8MMP3i2vo6NDOTFo/PjxRqPRu0O7wxRD6E1XrlwZrMFT040bN1xmQUQz6TymOn/+fOcx1YaGhsFmJryuurp6sDICE0PoHXa7fdWqVeKPHiIjI3fs2GG1WuWWVFtb+8QTTyjnA6xevVpM8cXFxe3fv7+/v1887datWy4zE/c0b3l/HA7Hxx9/nJiYKNr1hx56KJBnFBlC79i5c6cy8ODc4ElXVlY2a9YsAFFRUS4HY6J3nThxotK7Os9MqEAM7YrudM6cOWq+taYwhN6xbNkyAOnp6bILcaO3t3fy5MkAnE+Oq6ioWLRokTIzcerUKVnlffTRRwBGjBghqwDpeHkL75g/fz6A1atXyy7EjdDQ0JiYGAAJCQnikaKiosWLF1dWVqakpHzyyScVFRXKVKH6xPZLVBiYGELvGD16tPIVwLp16x5//PHu7m6pRQ1q/fr1UVFRBQUFNTU1mzZtUv/ibnv37t26dWt1dTUAMYAkvgYmXt7CJ8rLyy0WS39/v+xC3EtLS7t8+bKyyVDfkSNHDh48mJOTk5GRIasG7eCe0DsGtH290DvLk5hAcsEQkmQa336pgCEkkowh9Alu3T3j78cZQ0gkGUPoHRrftGu8vADHEJJk3EAwhESSMYQ+wa27Z/z9OGMIiSRjCL1D45t2jZcX4BhCkowbCIaQSDKG0Ce4dfeMvx9nDCGRZAyhd2h8067x8gIcQ0gkGUNIknEvzRD6BD9YnvH344whJJKMISSSjCH0Do33VxovL8AxhAGqvr5edgm3cQPB6476xK5du/r6+sRNNjXo2LFjOTk5Gzdu3LNnT3JysvoFbNiwITU1NTU1Vf231iCNfkqGu9dee012CZ7U19eHhYUdPHjwq6++2rZt26uvvipuJ6wavV6v/Ftcp1yzVytXAdtRL7BarSdOnABw/Pjx3t5e2eW4amlpaW9vB/D999+LR15++eWGhga9Xm+xWLZt25aamiruyiLFZ599BqCrq0tWAfKpePMZP+R8n73o6GgAU6dOPXTokOy6buvt7TUajeJi22Jft3bt2p9++kl5Qnl5eXp6uvgkrFq1SuWbBF6/fj0/P18cDWZlZan51prCEN4/lxvfvvXWW8oHOjs7W/pdL0tLS6dMmSLqyc3NfeONN8Qt40NDQ++8S6GHO2z7Qm9v7+7du5Vb2GdmZprNZl+/qWYxhPejtbXVYDAoN741mUzi3ut2u91kMsXGxgIIDg42GAzt7e3ql+d8j960tLSvv/5aPN7S0qKUHR8fbzKZlPv1dnV1KXfYjomJUe6w7QvKfUsB5OTk1NTU+OiNhguG8N6IBk/ZhDvvUhRdXV35+fliaDQmJsZoNNrtdnXKEw2euPft2LFjjUaj2Do4O3369NKlS0UGMjMzv/vuO2XRhQsXcnNzxaIZM2YcPnzYu+U5rz81NfWLL77w7vqHKYbwHjg3eGvXrr106ZKHJ9fV1T355JPiyTNnzvzyyy99WpvNZjMajVFRUWLrYDAYOjo6Bnuyw+EoKSkRkxM6nS4vL6+5uVlZ6rKnqq2tffDy1NzTDjsM4ZDU1tYqd+G9p0SVlpZOnTpVya3zoIgXlZWVzZ49+14bPIvFUlhYKAZsRo4cWVhY2N3dLRaJSHve4Q+ROOYULbpqx5zDC0N4F52dnS4N3r32ls4faJdBkQdXX1+/Zs0apcFzviv9EJnNZmXWLikpqbi4WFnU0dGh/Ozjx49329x6dvToUYmjr8MFQzgom81mMpnGjx+vjLJ4aPDu6s6xHGVQ5P7cuHFDafCio6OLiop6enrue23Hjh0bLC3V1dXOg8AnTpwYygovXryYl5cnXpWcnOycbXLBELpXVlb28MMPi8/QY4899uOPP3pltVVVVVlZWW4HRYbOR5MKzn3jnUO7paWlKSkpyjFkU1PTYOvx0OWSWwyhq4aGBmUTPn369JKSEu+u3/OgyF0577JWrlx59uxZ75bnYWjXYrFs3749IiICQEZGxp2vdTgcxcXFkyZNEj+aXq9va2vzbnl+iSH8H9HghYWFARg1alRhYeGDNHieWa1WZXcRGRk5lN2Fh4M3r7tw4cJgQ7uNjY0bN268c2iqsrJy8eLF4iULFiw4efKk78rzMwzhwMCvndjEiROVBu/q1asqvO8QcyWrwXMZ2h1sSubKlSt6vV6cfZaQkFBcXOxwOFQoz28whAPl5eVz584VH7UVK1acOXNG5QI8DIqI3jUpKen+etcH53muwmq1FhUVjRo1CkBERERBQcGtW7fULM8/BHQInXdEiYmJEjfhLpNpzz77bH19/eHDh5csWaI0ePc3iuMVLkO7b775pt1uP3DgwOTJk5X9ZGNjo6zyhrvADeHZs2eVBm/Xrl1aGMHr7OxUDqucvfvuu1po8CoqKhYtWiRKEoEUIzRDnLSgwQRuCB0Ox/Lly9Vv8DzbtWuX2C6I/lOMEmnnFBOHw/H++++L6fugoKAdO3bc6/Q93Slw/7Jep9N988034lOuNa+88sq6devGjRu3dOlS8fe4GqHT6bZu3fr0009XVVXNmzcvISFBdkX+IHBDCECbCRQWLlwou4RBTZw4UTlXjh4cL2+haQMBfyWyQMAQEknGEBJJxhBqC/vPAMQQahozGQgYQiLJGEIiyRhCIskCerJeg14LDy9ISrL/ehZBS1ycLiKCW0r/xhBqS0R3Ny5fDrHZxLehra3o7AQHZvwaN7JEkjGERJIxhESSMYQaMzAAgAeBAYUh1DZmMgAwhESSMYREkjGEGsP+M/AwhESSMYREkunEX6yRVrS1obUVcXGIjweAc+fQ14e5cxHMEwz9Fv9rNWbMGJSW4vhxXL2K8HCkpmLTJibQv3FPqCXV1Vi/Hi0tmDoVqamwWnH6NLq78cILMJkQGiq7PvIJbmI149o15ObCasWnn+KZZ24/2NUFgwEffoiYGLz5ptT6yFe4J9SM11/Hnj3YuxcvvfSbx202PPIIGhrw3/8iKUlSceRDHB3VjH//G2PG4Pe/d308NBR//CP6+vCf/8goi3yOIdSGmzfR1IT0dLi9Mr+4F9K5cyoXRepgCLXhxg0AGDfO/dLYWADo7FSvHlIRQ6gNISEA0NfnfqndDsD9TpKGP4ZQG2JjERICs9n90uZmALen78nvMITaEBaGjAzU1qKtzc3SsjIAyMpSuShSB0OoGS++iP5+FBa6Pm42w2TCpEl46ikZZZHPMYSa8cILWLEC+/ZhyxbU1wNAdzcOHcKKFbBY8N57PCb0V5ys1xKLBX/6Ew4cgMOBoCA4HAAQH4+338aGDbKLI19hCLWnuRnHj6OtDWFhSEvDihUID5ddE/kQQ0gkGY8JiSRjCIkkYwiJJGMIiSRjCIkkYwiJJPt/YATcVZiZEw8AAAEdelRYdHJka2l0UEtMIHJka2l0IDIwMjQuMDMuMwAAeJx7v2/tPQYgEABiRgYI4AViPiBuYGRjSACJM7MxaABpZhY2hgwQzcwIE+CA0ExsDlAFDjAF+Bgww1DN4AY6gJFJg4mJWYGZRYOJmVWBlS2DiY09gZ0jg4mDM4GTK4OJi1uBm0eDiZtRgYs1wYkJqI+NkZuLFegANnYOTi5W8WVI/mDgnckgeODCzez9IA6j9Ob9Lkw39oHYpxg277/3T9gexNY35dhv8bQTzD7EwLF/zicWBxC7/+gqO7nFEDXTps+0j9gOUXPCkMUh1h0ivlaFxcG18CbYTPXkmfYBE3PAdm2KWWV3dCfELu55HPsnmELEKy9x7D8h9Q3MFgMAdEI+2Hf3hrYAAAFZelRYdE1PTCByZGtpdCAyMDI0LjAzLjMAAHicfZNbboMwEEX/WcVsIJbn4ddnCFFVVQGpTbuH/mf/6gzEMZGc2oyF7YMH7h0GsPY5ffze4NFoGgYA/89VSoEf9t4PF7AbGM9v7zOcrsexrpyW7/n6BciAos9of2aP1+VSVxBOcCBHMcSIcPCuEFIh8M6vrT1LRqLjUiiSkUKYuyRXMku0/ZAy+twBxUDdZ/IKovMpcRcMsGwgWm56DUY90TtmDpzXzDF1uaQcOlQxlbPEoXS5rJymi6GeF/p5y52TpNyqjcSuNujvmTNvZCHxfRLv30KPM7nvDFLVkeNmTSF6kZ6rkmLeqEsv0fM8PVXKVjvjMk+tdqxTKxDr3KoANaR5TRqhOYo6jc03nUBq9ohGbi6IRmliiwbuNRUbEHfa4TrQTiOyAXknBa7LjwWxY7C9lCmw/16b1/9M74c/BIKzGEvjo7kAAACzelRYdFNNSUxFUyByZGtpdCAyMDI0LjAzLjMAAHicPY5BDsMgDAS/0mMiEYTXxmBVOfGAPCLfyONrQ1tOaDRrzRh0jrGd137jjgcH1+vZDmRoVU1HyQaCIb0PymyGyQTU/6yLppJr61S6I/8yiiJRLq0xfRHBEX6oZGau3OdQWwwpk5nJnFVzx2WtVaZSOZQg0mQViEZBrDrLCpUSKG6j+e2p8YqPBFZPmCL0D0UC0oJpfz5bOjh/VTb76wAAAABJRU5ErkJggg=='/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL1370561</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHEMBL1395</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEMBL1536</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL3989728</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHEMBL411</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHEMBL439</td>\n",
       "      <td><img src='data:image/png;base64, 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'/></td>\n",
       "      <td><img align=\"left\" src=\"\"></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(sim_df.drop(columns=[\"SMILES\"]).head(10).to_html(escape=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7044994c-7943-4467-8ca8-830299e908d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8f0b92-825d-412e-b60b-c52f41b230d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
